Introduction

The fishHook R package enables agile statistical exploration of coding and non-coding mutational recurrence in cancer through generalized linear modeling (GLM) of heterogenous neutral somatic mutation rates along the genome. fishHook can be applied to the analysis of genes, enhancers, promoters, genomic tiles, or any arbitrary “hypothesis set” (defined by the user as a set of genomic intervals or GRanges), as well as complex sets of the above (e.g. genes sets representing pathways, enhancers sets known to interact with a target gene). The core of the fishHook approach is to enable nomination of loci following the correction of known covariates of neutral mutation, including chromatin state, replication timing, and nucleotide context. The goal of fishHook is to identify cancer “drivers”, i.e. loci that are under positive somatic selection through the application of an accurate null / background model whose application yields straight Q-Q plots.

Though we provide many pre-computed covariates, the key power of fishHook is to empower the user to add custom covariates and (more broadly) provide a framework for generation of custom models, e.g. fitting variant- and tumor-type specific linear models and then integrating deviations from these models to nominate loci (or sets of loci) that deviate from the NULL.

Please include this citation if you use fishHook: Imielinski, Guo, Meyerson Cell. 2017 Jan 26;168(3):460-472

Driver discovery in cancer whole exomes

We will demonstrate a quick whole exome analysis using public TCGA lung adenocarcinoma mutation data. Additional packages like gTrack and rtracklayer will help with data import and visualization, but are not necessary to run fishHook.

library(fishHook)    
library(gTrack)
library(plotly)
library(rtracklayer)

Read in data

Read in the mutation data and additional tracks that we will use in this analysis.

## mutation calls cached from public GDAC Broad firehose https://bit.ly/2sFxWY6
mutations = dt2gr(fread('http://mskilab.com/fishHook/hg19/luad.maf'))

## GENCODE v19 genes these are our "hypotheses"
genes = gr.sub(import('http://mskilab.com/fishHook/hg19/gencode.v19.genes.gtf')) %Q% (gene_type == 'protein_coding') %Q% (level<3)

## protein coding cds definitions
cds = readRDS(gzcon(file('http://mskilab.com/fishHook/hg19/gencode.v19.cds.rds')))

## bigWig file of fractional coverage of hg19 positions by Agilent exome
## we will use this in combination with cds to define eligible positions
exomecov = import('http://mskilab.com/fishHook/hg19/exome_coverage.bw')

Take a peek at our mutations

head(mutations[, c('Tumor_Sample_Barcode', 'Variant_Type', 'Variant_Classification', 'Reference_Allele', 'Tumor_Seq_Allele2')])
## GRanges object with 6 ranges and 5 metadata columns:
##       seqnames    ranges strand |         Tumor_Sample_Barcode
##          <Rle> <IRanges>  <Rle> |                  <character>
##   [1]        1    905907      * | TCGA-05-4249-01A-01D-1105-08
##   [2]        1   1192480      * | TCGA-05-4249-01A-01D-1105-08
##   [3]        1   1854885      * | TCGA-05-4249-01A-01D-1105-08
##   [4]        1   9713992      * | TCGA-05-4249-01A-01D-1105-08
##   [5]        1  12908093      * | TCGA-05-4249-01A-01D-1105-08
##   [6]        1  17257855      * | TCGA-05-4249-01A-01D-1105-08
##       Variant_Type Variant_Classification Reference_Allele
##        <character>            <character>      <character>
##   [1]          SNP      Missense_Mutation                A
##   [2]          SNP                 Silent                C
##   [3]          SNP                    IGR                G
##   [4]          SNP                 Intron                G
##   [5]          SNP      Missense_Mutation                C
##   [6]          SNP      Missense_Mutation                C
##       Tumor_Seq_Allele2
##             <character>
##   [1]                 T
##   [2]                 A
##   [3]                 C
##   [4]                 A
##   [5]                 A
##   [6]                 T
##   -------
##   seqinfo: 25 sequences from an unspecified genome

Instantiate FishHook object from events and eligible territory

First we define the “eligible territory”. This is a key component of all somatic mutational recurrence analyses, since not all of the genome is covered by most sequencing assays. For example in a whole exome sequencing dataset, less than 2% of the genome is reliably captured. In a targeted sequencing panel, this fraction will be even smaller. Even in whole genome sequencing using Illumina short reads, only a subset (70%) of the genome is reliably callable (Heng Li Bioinformatics 2014 Oct 15;30(20):2843–51)

Eligible territory coverage will influence the “denominator” of our recurrence analysis, i.e. the number of positions in each hypothesis interval where a mutation could have possibly been detected. If we do not take eligible territory into account we will mis-estimate the background mutation rate in a given region.

To define eligible territory for this whole exome analysis, we will choose the portion of cds (protein coding) bases that are captured in at least 95% of whole exomes, which represents about 26MB of genome.

eligible = reduce(intersect(exomecov %Q% which(score>0.95), cds, ignore.strand = TRUE))

We define “events” as nonsynonymous mutations. In this simple model, we will lump together SNVs (of different flavors), and indels (of different flavors). (We discuss more complex models that subdivide mutation types later in the tutorial).

events = mutations %Q% (Variant_Classification != 'Silent')

Now that we have loaded our hypotheses (genes), events, and eligible, we are ready to create and analyze a basic FishHook object. Instantiation of the object will involve counting how many events are in the eligible portion of each hypothesis interval. We provide the “idcol” parameter so that each tumor sample (as defined by the “Tumor_Sample_Barcode” column in this events GRanges) can only provide at most one event to the counts in each interval.

fish = Fish(hypotheses = genes[, 'gene_name'], events = events, eligible = eligible, idcol = 'Tumor_Sample_Barcode')
## Contains 19796 hypotheses.
## Contains 56809 events to map to hypotheses.
## Spanning 24.58 MB of eligible territory.
## Covariates:
## 
## Hypotheses contains 1 metadata columns.
## Current State: Annotated

Run basic FishHook model without covariates

We can score this basic FishHook object, i.e. compute p values for every hypothesis, using a simple glm that models a uniform mutation density along the genome, i.e. the glm fits only an intercept and applies no covariates (after correcting for the number of eligible bases in each interval).

The $res field of the FishHook contains a data.table of scoring results. $res has one row per input hypothesis, with p values, FDRs, and additional interval annotations provided with the hypotheses GRanges.

fish$score()
head(fish$res[order(p), ])
fish$qqp()
seqnames start end width strand gene_name p fdr count effectsize count.pred count.density count.pred.density p.neg fdr.neg
17 7565097 7590856 25760
TP53 0.0e+00 0.0000 106 5.62 2.15 0.0941 0.00191 1 1
2 79384132 79386879 2748
REG3A 1.0e-07 0.0009 17 4.09 1.00 0.0324 0.00191 1 1
19 10596796 10614417 17622
KEAP1 5.0e-07 0.0031 30 3.54 2.57 0.0222 0.00191 1 1
5 24487209 24645087 157879
CDH10 3.6e-06 0.0180 41 3.19 4.50 0.0174 0.00191 1 1
3 147103833 147124647 20815
ZIC4 5.5e-06 0.0200 18 3.41 1.69 0.0203 0.00191 1 1
8 88882973 88886296 3324
DCAF4L2 6.4e-06 0.0200 22 3.28 2.26 0.0186 0.00191 1 1

You will notice that this Q-Q plot appears curved and inflated, though its slope (lambda) is reasonably near 1. The low alpha value (MLE of the alpha parameter of the Gamma distribution), suggests that the GLM is detecting additional variance in the data that is unmodeled by a pure Poisson regression. Adding covariates to the model should improve the quality of the fit.

The top hits in the plot (you can hover over them) include TP53 but also many unlikely cancer gene candidates. Among these are olfactory receptors, which are located in late replicating regions of the human genome and thus accumulate neutral mutations more frequently (Lawrence et al 2013 Nature Jul 11;499(7457):214-218).

Add covariates to FishHook model

To address these issues, we will load in data specifying replication timing, chromatin state, and nucleotide context. These are all important determinants of somatic neutral mutation density. We load these data in as GRanges objects using functions in data.table, rtracklayer, and gUtils packages (however you are free to use any GRanges import utility of your choice).

We first load in replication timing data as a GRanges then instantiate it as a Covariate Replication timing information is contained in the $score metadata field of reptime. We instantiate it as a covariate of type “numeric” by specifying field score.

## replication timing for NHEK obtained from  https://bit.ly/2sRsXT9 and
## converted to rds via rtracklayer::import
reptimedata = readRDS(gzcon(file('http://mskilab.com/fishHook/hg19/RT_NHEK_Keratinocytes_Int92817591_hg19.rds')))

reptime = Cov(data = reptimedata, field = 'score')

Below, context is a GRanges object with 98 columns representing tri, di, and mononucleotide context counts in the hg19 genome. Code for computing context (e.g. for another genome) is provided here.

We instantiate a numeric Covariate object from context, choosing only two of the columns here to take into account G and C content. Note that the covariate object can be vectorized (concatenated, subsetted) and instantiated around several columns of an input GRanges. As a result, contextcov will be length 2 (representing G and C nucleotides fraction).

context = readRDS(gzcon(file('http://mskilab.com/fishHook/hg19/nucleotide.context.rds')))
gc = Cov(data = context, field = c('C', 'G'))

Finally we load in chromHMM data for cell line A549 from Epigenomics Roadmap. We will want to create a covariate that will model the fraction of heterochromatic and quiescent regions in each query interval.

To do so, we will create an “interval” covariate by not specifying a metadata field.

### data cached from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/E114_15_coreMarks_mnemonics.bed.gz
hetchromdata = gr.sub(import('http://mskilab.com/fishHook/hg19/E114_15_coreMarks_mnemonics.bed.gz'), 'chr', '') %Q% (name %in% c('8_ZNF/Rpts', '9_Het', '15_Quies'))
hetchrom = Cov(hetchromdata, name = 'Heterochromatin')

We now add these covariates to the model. For type numeric covariates, e.g. replication timing, this will trigger the calculation of the average value of each covariate within the eligible subset of each hypothesis interval. and the fractional overlap of of its eligible subset This annotation is the most computationally intensive and slowest aspect of fishHook analyses, though occurs within a few seconds for this small number of covariates.

fish$covariates = c(reptime, gc, hetchrom)
## FishHook 2018-06-11 19:49:06: Aggregating covariates over eligible subset of hypotheses
## FishHook 2018-06-11 19:49:06: Overlapping with covered intervals
## FishHook 2018-06-11 19:49:06: Finished overlapping with covered intervals
## FishHook 2018-06-11 19:49:06: Annotating track score
## FishHook 2018-06-11 19:49:11: Annotating track C
## FishHook 2018-06-11 19:49:13: Annotating track G
## FishHook 2018-06-11 19:49:16: Annotating track Heterochromatin
## Warning in self$clear(): Resetting scores since covariates re-defined,
## please run $score() to get updated p values

Now that we’ve added covariates, we can rescore fish and compute p values. Looking at these results, we immediately see an improvement in lambda (closer to one) and alpha (increased), and the nominated gene list (no more olfactory receptors, We see also a reasonable number of significant (fdr<0.1) genes at the top of the list (or at the top right of the QQ plot) that have been biologically implicated in lung adenocarcinoma tumorigenesis.

fish$score()
fish$res[order(p)[fdr<0.1, ]
fish$qqp()
seqnames start end width strand gene_name p fdr count effectsize count.pred count.density count.pred.density p.neg fdr.neg
17 7565097 7590856 25760
TP53 0.0e+00 0.00000 106 6.27 1.370 0.09410 0.00121 1 1
19 10596796 10614417 17622
KEAP1 0.0e+00 0.00000 30 4.02 1.840 0.02220 0.00137 1 1
7 55086714 55324313 237600
EGFR 1.0e-07 0.00054 44 3.06 5.280 0.01200 0.00144 1 1
7 142457319 142460923 3605
PRSS1 6.9e-06 0.03400 16 3.06 1.910 0.02160 0.00258 1 1
1 157800704 157868046 67343
CD5L 8.7e-06 0.03400 14 3.07 1.670 0.01340 0.00161 1 1
21 44513066 44527697 14632
U2AF1 1.8e-05 0.05400 8 3.38 0.768 0.01180 0.00113 1 1
14 19553365 19590078 36714
POTEG 2.0e-05 0.05400 13 2.99 1.640 0.02210 0.00279 1 1
11 62443970 62446567 2598
UBXN1 2.2e-05 0.05400 10 3.28 1.030 0.01140 0.00118 1 1
1 175284330 175712906 428577
TNR 2.8e-05 0.05700 38 2.49 6.740 0.00933 0.00166 1 1
6 136578001 136610989 32989
BCLAF1 2.9e-05 0.05700 23 2.66 3.650 0.00834 0.00132 1 1
7 140419127 140624564 205438
BRAF 3.6e-05 0.06500 21 2.66 3.320 0.00984 0.00156 1 1
8 10463859 10569697 105839
RP1L1 4.6e-05 0.07600 48 2.34 9.450 0.00791 0.00156 1 1
19 10416103 10426691 10589
FDX1L 5.3e-05 0.08100 4 4.02 0.247 0.01650 0.00102 1 1

Note that this model is still quite rudimentary (e.g. we have lumped together SNVs and indels, we have not substratified SNVs by mutational context, we have employed very few covariates) but we still get a reasonable gene list and minimal statistical inflation.

We can inspect the parameters of this model see which features it’s using. We can see from the estimate and Pr(|>z|) that replication timing is significantly negatively correlated and heterochromatin is significantly positively correlated with mutational density (as expected).

summary(fish$model)
## 
## Call:
## glm.nb(formula = formula, data = as.data.frame(tdt), maxit = iter, 
##     init.theta = 3.514844957, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.1965  -0.9862  -0.2834   0.4381  15.5196  
## 
## Coefficients:
##                 Estimate Std. Error  z value Pr(>|z|)    
## (Intercept)     -6.63330    0.04891 -135.631  < 2e-16 ***
## score           -0.51553    0.01023  -50.411  < 2e-16 ***
## C                0.70117    0.41074    1.707 0.087801 .  
## G                1.47342    0.41306    3.567 0.000361 ***
## Heterochromatin  0.33214    0.01672   19.860  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(3.5148) family taken to be 1)
## 
##     Null deviance: 23853  on 18186  degrees of freedom
## Residual deviance: 19088  on 18182  degrees of freedom
## AIC: 65310
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  3.5148 
##           Std. Err.:  0.0935 
## 
##  2 x log-likelihood:  -65298.0760

Analyze truncating mutations and pathways

We can run a similar analysis but choosing only truncating mutations (by subsetting the mutation GRanges). Scoring this new model, we obtain a different mutation list, containing likely candidate drivers with enrichment of frameshift, nonsense, or nonstop indels and SNVs. This includes genes like ARID1A, RBM10, and SETD2.

## replace events with new subset of mutations
fish$events = mutations %Q% 
                 (grepl('(Frame_Shift_)|(Nonsense)|(OutOfFrame)|(Nonstop)', Variant_Classification))

## re-score model and inspect results
fish$score() 
head(fish$res[order(p), ])
fish$qqp()
seqnames start end width strand gene_name p fdr count effectsize count.pred count.density count.pred.density p.neg fdr.neg
17 7565097 7590856 25760
TP53 0.0e+00 0.00000 33 7.72 0.1570 0.02930 0.000139 1 1
3 47057919 47205457 147539
SETD2 0.0e+00 0.00014 18 4.23 0.9620 0.00295 0.000157 1 1
X 47004268 47046212 41945
RBM10 1.0e-07 0.00038 6 5.52 0.1310 0.01140 0.000249 1 1
16 3115298 3131908 16611
IL32 1.2e-06 0.00590 4 5.61 0.0820 0.00905 0.000186 1 1
8 126442563 126450647 8085
TRIB1 3.7e-06 0.01500 4 5.48 0.0898 0.00531 0.000119 1 1
1 27022524 27108595 86072
ARID1A 4.8e-06 0.01600 10 3.84 0.6980 0.00176 0.000123 1 1

Read in and parse reactome pathways as list of gene symbols.

## parse Reactome pathways from .gmt format
pathways = strsplit(readLines('http://mskilab.com/fishHook/hg19/ReactomePathways.gmt'), '\t')
pathways = structure(lapply(pathways, '[', -1), names = sapply(pathways, '[', 1))

## match them to create sets of indices as a named list
sets = sapply(sapply(pathways, match, fish$hypotheses$gene_name), setdiff, NA)

Here is what the pathways and sets look like:

head(pathways[1:2])
## $`2-LTR circle formation`
##  [1] "R-HSA-164843" "BANF1"        "HMGA1"        "LIG4"        
##  [5] "PSIP1"        "XRCC4"        "XRCC5"        "XRCC6"       
##  [9] "gag"          "gag-pol"      "rev"          "vif"         
## [13] "vpr"          "vpu"         
## 
## $`5-Phosphoribose 1-diphosphate biosynthesis`
## [1] "R-HSA-73843" "PRPS1"       "PRPS1L1"     "PRPS2"
head(sets[1:2])
## $`2-LTR circle formation`
## [1] 10815  6342 12670  8606  5385  3035 18801
## 
## $`5-Phosphoribose 1-diphosphate biosynthesis`
## [1] 19432  7085 18957

The set analysis is a bit more computationally intensive. We can speed things up through parallelization (setting fish$mc.cores = 5). When we set the sets variable to a particular value, this will trigger automatic scoring of hypothesis sets (in this case gene sets). The results are shown in $setres variable.

setname p fdr effect estimate p.left p.twosided
TP53 Regulates Transcription of Genes Involved in G1 Cell Cycle Arrest 1.11e-29 0 16.5 [10.1-27] 16.5 1 0
RUNX3 regulates CDKN1A transcription 1.24e-29 0 30.6 [16.9-55.7] 30.6 1 0
Formation of Senescence-Associated Heterochromatin Foci (SAHF) 2.78e-29 0 14.2 [8.89-22.5] 14.2 1 0
Activation of PUMA and translocation to mitochondria 2.93e-29 0 26.2 [14.8-46.4] 26.2 1 0
TP53 Regulates Transcription of Caspase Activators and Caspases 3.67e-29 0 16.9 [10.3-27.8] 16.9 1 0

Examining the results table, we can see that most of the significant pathways appear related to TP53. Indeed, if we inspect the hypotheses (i.e. genes) contributing to these these gene sets, we will see that they are dominated by TP53 and 1-2 additional genes. For example:

fish$res[sets[[fish$setres[order(p), ][fdr<0.1,  ][3, setname]]], ][order(p), ]
seqnames start end width strand gene_name p fdr count effectsize count.pred count.density count.pred.density p.neg fdr.neg
17 7565097 7590856 25760
TP53 6.9e-38 0.00 33 7.72 0.1570 0.029300 0.000139 1.00 1
13 48877887 49056122 178236
RB1 0.00016 0.24 6 3.58 0.5030 0.002180 0.000183 1.00 1
5 126112315 126172712 60398
LMNB1 0.061 1.00 1 1.59 0.3320 0.000768 0.000255 0.95 1
12 66217911 66360075 142165
HMGA2 0.081 1.00 0 -Inf 0.0275 0.000000 0.000137 0.97 1
6 26017260 26018040 781
HIST1H1A 0.24 1.00 0 -Inf 0.2390 0.000000 0.000370 0.80 1
6 27834570 27835359 790
HIST1H1B 0.29 1.00 0 -Inf 0.2020 0.000000 0.000311 0.82 1
6 36644305 36655116 10812
CDKN1A 0.46 1.00 0 -Inf 0.0543 0.000000 0.000140 0.95 1
16 4896666 4932361 35696
UBN1 0.48 1.00 0 -Inf 0.4690 0.000000 0.000138 0.65 1
22 24407642 24574596 166955
CABIN1 0.57 1.00 0 -Inf 0.7160 0.000000 0.000140 0.54 1
6 34204650 34214008 9359
HMGA1 0.59 1.00 0 -Inf 0.0186 0.000000 0.000132 0.98 1
6 26234440 26235216 777
HIST1H1D 0.67 1.00 0 -Inf 0.1890 0.000000 0.000285 0.83 1
22 19318221 19435224 117004
HIRA 0.69 1.00 0 -Inf 0.5410 0.000000 0.000163 0.62 1
22 38201114 38203442 2329
H1F0 0.71 1.00 0 -Inf 0.0719 0.000000 0.000132 0.93 1
6 26156559 26157343 785
HIST1H1E 0.76 1.00 0 -Inf 0.1260 0.000000 0.000355 0.88 1
6 26055968 26056699 732
HIST1H1C 0.88 1.00 0 -Inf 0.2010 0.000000 0.000314 0.83 1
6 119215384 119230332 14949
ASF1A 0.97 1.00 0 -Inf 0.1170 0.000000 0.000275 0.89 1

This is a common challenge with pathway analysis of mutations, since many cancer pathways are usually driven by a single “celebrity” gene.